The authors in [1]} proposed the Dynamic Convolutional Layer for short-range weather prediction task. In particular, in contrast to traditional CNN approaches in Dynamic Convolutional Layer the filters vary from input to input during testing. RNNs, on the other hand, generally perform well when dealing with time-series data [2]}. In particular, LSTM networks can better model long-term dependencies [3]}. Long- and Short-term Time-series network (LSTNet) [4]} leverages both CNN and RNN to extract short-term local dependency patterns among variables and to discover long-term patterns for time series trends. The ConvLSTM network combines both CNN and LSTM architectures for precipitation prediction [5]}. However, the sequential nature of processing elements in RNNs limits the parallelization during training, which becomes critical at longer sequence lengths [6]}.
